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您的检索式：作者名="Fiona Whyte"
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| 1 | 指导性集体锻炼对治疗中的早期乳腺癌妇女的益处:实用性随机对照试验显示文摘目的确定12周指导性集体锻炼对治疗中的早期乳腺癌患者在机体功能和心理上的获益,并随访6个月。设计实用性、前瞻性、随机对照、开放性试验。地点苏格兰3家国家医疗卫生服务体系肿瘤门诊和社区运动设施。参加者共203位妇女参加研究;其中177位完成6个月随访。干预与常规护理相对照,干预组予12周指导性集体锻炼加常规护理。主要结果评价癌症治疗功能评价(FACT)问卷、贝克抑郁问卷、正性和负性情绪量表、体重指数、7日体力活动回忆问卷、12分钟步行实验和肩关节活动度评估。结果对基线值、研究中心、基线治疗和参加干预试验时的年龄进行校正后,综合效应模型的评估结果(干预组减对照组)显示第12周时:12分钟步行距离为129米(95%可信区间83~176),1周的中等强度活动时间182分钟(75~289),肩关节活动度2.6(1.6~3.7),乳腺癌特定的生活质量量表2.5(1.0~3.9),正性情绪4.0(1.8~6.3)。初步结果未发现干预锻炼对生活质量综合量表(FACT-G)有影响。随访6个月后,大多数效果维持不变,并且在乳腺癌特定的生活质量上得到改善。尚未有不良反应的报道。结论在12周干预锻炼结束时以及6个月后,指导性集体锻炼均显示出机体功能和心理上的获益。因此临床医生应该鼓励患者活动,决策者们应该考虑把体育锻炼纳入到癌症康复服务中来。试验注册号当前对照试验 ISRCTN12587864[controlled-trial.com]。 | Nanette Mutrie Anna M Campbell Fiona Whyte Alex McConnachie Carol Emslie Laura Lee Nora Kearney Andrew Walker Diana Ritchie 方桦(译) 王燕(校) | 2007 | 英国医学杂志中文版2007，10，4： | 2 |
| 2 | Pregnancy‐Associated Osteoporosis With a Heterozygous Deactivating LDL Receptor‐Related Protein 5 ( LRP5 ) Mutation and a Homozygous Methylenetetrahydrofolate Reductase ( MTHFR ) Polymorphism显示文摘 | Fiona J Cook Steven Mumm Michael P Whyte Deborah Wenkert | 2014 | J Bone Miner Res2014，，4： | 1 |
| 3 | Prediction of voltage distribution using deep learning and identified key smart meter locations显示文摘The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers. | Maizura Mokhtar Valentin Robu David Flynn Ciaran Higgins Jim Whyte Caroline Loughran Fiona Fulton | 2021 | Energy and AI2021，6，4： | 0 |
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